Method for calibrating at least one sensor, in particular a pressure sensor, having at least one signal-conducting connection to at least one signal converter

ABSTRACT

In a method for calibrating at least one sensor having at least one signal-conducting connection to at least one signal converter, a sensor characteristic is recorded by the determination of at least one measurand at at least two different temperatures, the extent of the influence of a further value influencing the sensor is determined from the sensor characteristic by means of a functional relation, the extent of the influence of the further influencing value is considered in the calibration and the influence of the further influencing value is balanced in the calibration. As a result, the influence of a further influencing value acting on the sensor is corrected. The sensor can be a structure borne sound sensor which may have a membrane with a sensor element arranged on the membrane. Vibrations on the membrane can be captured by the sensor element, such as a piezoresistive element.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-in-Part of co-pending applicationSer. No. 15/016,955 filed on Feb. 5, 2016, for which priority is claimedunder 35 U.S.C. § 120; and this application claims priority ofApplication No. 10 2015 001 500.1 filed in Germany on Feb. 5, 2015 under35 U.S.C. § 119; the entire contents of all of which are herebyincorporated by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The invention relates to a method for calibrating at least one sensor,having at least one signal-conducting connection to at least one signalconverter.

The calibration of sensors is required for outputting exact values bythe sensor. A standard method for calibrating a sensor or also anothermeasuring device provides that known reference values of the measurandto be determined are provided to the sensor, to which the sensor is thencorrespondingly set.

Brief Description of the Related Art

For example DE 01 2006 058 269 D4 describes a method for calibrating atleast one pressure sensor. Here, the pressure sensor to be calibratedcomprises at least one membrane. First, a process pressure applied tothe membrane is measured by the pressure sensor. Said process pressureis superposed with an additional pressure and the pressure resulting atthe membrane is measured. Here, essentially the magnitude of thepressure, by means of which the process pressure is superposed, is to beequal to the measured process pressure. The resulting pressure value iscompared to a preset setpoint value, wherein the preset setpoint valueessentially is to be equal to zero. The deviation between the setpointvalue and the measured superposed pressure value is determined and usedfor calibration.

This comes with the disadvantage that, e.g. in the case of afactory-provided calibration, influence of influencing values acting onthe sensor after calibration cannot be considered. For example, sensorswhich are in a dry state directly after production and which arecalibrated in said state may systemically indicate wrong values afterabsorbing humidity, e.g. air humidity. Mostly, calibration after removalof moisture is not reasonable from an economic point of view sinceabsorption of humidity often times is a slow process with a saturationtime of several days and thus the later calibration takes too long forthe production process.

SUMMARY OF THE INVENTION

It is the object of the invention to provide a method for calibrating asensor in which influences of a further influencing value acting on thesensor can be corrected.

Said object is achieved by means of a method having the features of thepresent claims. Further developments and advantageous embodiments areindicated in the respective sub claims.

In a method for calibrating at least one sensor having at least onesignal-conducting connection to at least one signal converter, it isprovided according to the invention that a sensor characteristic isrecorded by determination of at least one measurand at at least twodifferent temperatures, that the extent of the influence of a furthervalue influencing the sensor is determined from the sensorcharacteristics via a functional relation, that the extent of theinfluence of the further influencing value is considered in thecalibration and that the influence of the further influencing value iscompensated.

The sensor can be a piezo-resistive pressure sensor, for example, whichis used for determining the pressure and/or the temperature of an engineoil in an engine, for example. The pressure applied by the engine oil istransformed in the sensor to a digital equivalent value, e.g. by meansof an integrated circuit. In consideration of the prevailingtemperature, the digital equivalent value can be calculated into thecorrect value of the pressure, e.g. in the measurement unit bar. Acorresponding measuring signal can be output as a pulse width modulationsignal, for example.

Furthermore the sensor can be a structure borne sound sensor. Astructure borne sound sensor might have a membrane, in particular aplastic membrane with a sensor element arranged on the membrane.Vibrations, propagating on the membrane, can be captured by the sensorelement. The sensor element can for example be a piezoresistive element,by which the vibrations can be detected. For an accurate capture of thevibrations, a consistent resonance frequency of the membrane isrequired. The captured vibrations can for example be transformed bymeans of an electrical circuit in digital equivalents values.

Calibration of sensors may be required due to production-causedvariations of the sensors, for example, which are reflected influctuations in the digital equivalent values of the parameter.

Values of at least one measurand, e.g. a pressure or a vibration, at atleast two different temperatures are recorded for determining a sensorcharacteristic. For example, digital equivalent values of two or threeapplied reference values, for example reference pressures or referencevibrations can be determined at three different temperatures.Additionally, the digitally equivalent values relative for the threereference temperatures can be determined. Said digitally equivalentvalues, referred to as raw values, serve as calibration input data. Theinput data may also include the reference temperatures and the referencemeasurement values. The output data of this calibration can be constantparameters which are stored in the integrated circuit, for example. Saidparameters can be considered as coefficients in a mathematical functionfor conversion of the digital equivalent values of the respectivemeasurands into the correct output values of the sensor. In case of thepressure sensor the mathematical function can be a polynomial. Anincorrect conversion of the digital equivalent values into therespective correct output values may result from the influence offurther influencing values such as the humidity. For example, pressureraw values in the dry state may exhibit a parabolic course depending onthe temperature. The raw values can be approximated by a polynomial.However, a different parabola is required for the approximation of theraw values after a moisture influence of the sensors than in the drystate. For investigation of this issue, for example a significantselection of identically constructed dry sensors can be considered in awet state and the respective measurement raw values of the sensorsmeasured by the sensors can be recorded depending on the temperatureduring drying of the sensors. An optimum characteristic line, e.g. aparabola, can be determined from the plurality of temperature-dependingcurves of measurement raw values, which parabola may be located onaverage between the curves of the humid state and the dry state, forexample. By means of this averaging process, prevention may be effectedagainst the influence of the further influence value, in particular ofmoisture, so that deviations of the raw data by approximation with awrong parabola become so small that the output values of the sensors arein the tolerance range of the sensor.

For example the membrane of a structure borne sound sensor, inparticular the resonance frequency of the plastic membrane, can beaffected by moisture. The resonance frequency has an influence to thesensitivity of the sensor. By the calibration process at differenttemperatures prevention may be effected against the influence ofmoisture.

The extent of the influence of the further influencing value, humidityfor example, on a sensor can be determined from the distance of themeasurement raw value curve in the dry state to the averaged optimum rawvalue curve. Determination of the distances between the two measurementvalues curves can be effected at three different temperatures, forexample. In order to not have to perform this method for every sensor tobe calibrated, the extent of the influence of the further influencingvalue can be determined form a sensor characteristic, such as thetemperature sensitivity of the raw values.

For this purpose, the secant of a raw value curve of a sensor isdetermined between at least two temperature values. The slope of thissecant indicates the sensitivity of the measuring signal relative to achange in temperature. This concept is referred to as sensitivity.

By means of the significant selection of sensors, a linear relationbetween the sensitivity and the extent of the influence of humidity atthe different temperatures was found. That means that the extent of theinfluence of the humidity can be concluded from the sensitivity that canbe determined from the sensor characteristic in a simple manner. Thisway, a compensation of the humidity influence can be calculateddepending on the sensitivity. For example the pressure raw valuesmeasured for calibration of each sensor are corrected in direction ofthe optimum averaged parabola by means of linear transformations whichhave been determined based on the significant selection of sensors. Thelinear equations of the linear transformation indicate the distance tothe optimum parabola at ambient pressure at the respective temperaturefor the sensitivity of a sensor. Hence, determination of the sensitivityof each individual sensor is sufficient for determining the correctivetransformation. At the calibration of pressure sensors a polynomial canbe calculated from the corrected raw data. The coefficients of thecalculated polynomial are stored and are used in the conversion of thedigital equivalent values of the measurand into the correct outputvalues.

In a further development of the method, the extent of the influence ofthe further influencing value corresponds to the intervals of the rawvalues of the measurand recorded by the sensor for respective averagedvalues on a predetermined characteristic line at at least twotemperature values. For example, the averaged values can be obtainedfrom a significant selection of identically constructed sensors, the rawvalues of which are recorded under the influence of a furtherinfluencing value. The further influencing value can be humidity, e.g.air humidity. For example, the raw values, in particular the pressureraw values can be recorded in a plurality of identically constructedsensors in a humid state while the sensors are dried. An optimumcharacteristic line, for example a parabola in the example of thepressure raw values, can be determined from this data, whichcharacteristic curve is on average between the measurement raw values ofthe sensors in the humid and in the dry state. The extent of theinfluence of the humidity on the sensor can be determined bydetermination of the distance of the raw data line of a sensor to theaveraged characteristic line.

In a further development of the method, the secant of the raw values ona functional graph of the measurand is calculated between at least twotemperature values, the slope of the secant is determined and thesensitivity of the raw values of the measurand to changes in temperatureis concluded from the slope of the secant. For example, the raw values,for example the pressure raw values of a sensor to be calibrated can berecorded in a range between 20° C. to 170° C. The secant can then bedetermined between two temperature points, e.g. between 40° C. and 130°C., from said temperature-depending raw values, for example the pressureraw value curve. The slope of this secant is determined. The slopeindicates the sensitivity of the measurement raw signal to thetemperature change of the pressure raw signal. This responsitivity tothe temperature change is referred to as sensitivity.

In a further development of the method a linear relation is assumedbetween the extent of the influence of the further influencing value andthe sensitivity of the raw values of the measurand to changes intemperature. The study of a plurality of identically constructed sensorsleads to the conclusion that there is a correlation between thesensibility of the of the raw value signal, for example the pressure rawvalue signal to the temperature change, hence the sensitivity, and theextent of the influence of the further influencing value, i.e. thehumidity, for example. A strong linear relation between the sensitivityand the importance of influence of the humidity can be concluded fromthis correlation. Said linear relation can for example be determined byaveraging the measuring results of a plurality of identicallyconstructed sensors at two temperature points, e.g. 40° C. and 80° C.,for example. The linear relation at these two temperature points cane.g. in each case be expressed by a linear transformation, e.g. in theform of a sensitivity-depending linear equation.

In a further development of the method, a compensation of the influenceof the further influence value in the calibration is calculateddepending on the temperature sensitivity of the measurand and the rawvalues are shifted in direction of the predetermined characteristic linefor compensating the influence. Linear transformation for differenttemperature points can be determined by correlation of the sensitivityof the measurement raw values to temperature changes with the extent ofthe influence of the further influencing value by evaluation of aplurality of identically constructed sensors. By means of said lineartransformations, the measured measurement raw values of a sensor can beshifted in direction of the characteristic line averaged for a pluralityof identically constructed sensors. By shifting the measurement rawvalues in direction of the averaged characteristic line, prevention iseffected against the influence of the humidity. Deviations of the rawvalues caused by the influence of humidity are thus compensated suchthat the measurement raw values can be correctly interpreted by thesensor and thus the output data of the sensor is within the precisiontolerances. In case of pressure sensors the measurement raw values canbe pressure raw values. In the case of structure borne sound sensors themeasurement raw values can be vibration raw values.

In a further development of the method, the correction of the raw valuesin direction of the predetermined characteristic line is effected bymeans of at least one linear transformation, which indicates thedistance to the predetermined characteristic line at at least onetemperature for at least one sensitivity value. The lineartransformations are determined by means of a significant selection ofidentically constructed sensors and depend on the respective sensitivityvalues. The linear equations at the respective temperature value arethus preset so that the sensitivity of the raw values to the temperaturechange is to be determined only for the calibration of one sensor bydetermining the slope of the secant from the sensor characteristics.

In a further development of the method, the predetermined characteristicline is an averaged characteristic line of raw values of the measurandat a varying temperature, which is determined at a plurality ofidentically constructed sensors, wherein the characteristic lines arerecorded while wet sensors are drying and the average value of thecharacteristic lines is determined. In terms of production, it isfavorable to calibrate sensors directly after production, i.e. in drystate. By using the sensors at normal atmospheric pressure, the sensorsare exposed to air humidity. Humidity has an influence on the conversionof the digital equivalent measuring values into the correct outputvalues, for example in the measurement unit bar or ° C. or the output ofthe correct vibration amplitude. In order to compensate inaccuracyduring conversion, a plurality of identically constructed sensors isexamined in the wet state while drying. Characteristic lines aregenerated from the respective measured measurement raw values, whereinan averaged characteristic lines between the humid state and the drystate is calculated. By means of said averaged characteristic line,prevention of the raw values measured by the sensor is made against theinfluence of humidity.

In one embodiment of the invention, there is a functional relationbetween the measured raw values and the output values of the sensor viaan at least first-order polynomial. The input data considered in thecalibration are the raw values, i.e. the digital equivalents of themeasurands, for example the digital equivalents of the measured pressurevalues, the digital equivalents of the temperature as well as of thereference temperatures and the reference pressure values. The raw valuesare transformed by the linear transformations, namely shifted indirection of the averaged linear characteristic line. This transformeddata represent the initial data for the calibration. For example in thecase of pressure sensors a polynomial can be calculated from thetransformed values and the coefficient of this polynomial are stored inthe integrated circuit as constant parameters. During later operation ofthe sensor, the raw values of the physical input values, for example ofthe pressure or the temperature, are converted into the respectivevalues in bar and ° C. by means of a polynomial. Here, the coefficientscalculated from the transformed raw values during calibration, whichhave been stored in the integrated circuit, are used as coefficients inthis polynomial. In order that the conversion of the raw values into therespective values in bar and ° C. is effected properly, the influence ofhumidity must be extracted by shifting the raw value curve in directionof the averaged curve.

BRIEF DESCRIPTION OF THE DRAWINGS

One embodiment of the invention, from which further inventive featuresresult, is shown in the drawing. The drawing shows in:

FIG. 1 illustrates a schematic graphic illustration of the measured rawvalues; and

FIG. 2 illustrates a method according to embodiment of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 shows the digital equivalents of the measurand, for example thepressure, at different temperatures in an exemplary manner. Here, attemperature T1 once pressure P1 and once pressure P3 were applied,leading to the record of digital equivalents 1 and 2 for the respectivepressures measured. At temperature T2, the three different pressures P1,P2 and P3 were applied, for which the respective digital equivalents 3,4 and 5 were recorded. Accordingly, at T3 the equivalents 6 and 7 forthe pressures P1 and P3 were recorded. The digital equivalents are alsoreferred to as raw values. In addition to the digital equivalents of themeasurand of the pressure, the digital equivalents of the temperatures,the reference temperatures T1, T2 and T3 as well as digital equivalentsof the reference pressures P1, P2 and P3 are considered in thecalibration. Thus, in this case, seven digital equivalents 1-7 of thepressure, three reference pressures, three digital equivalents of thetemperature and three reference temperatures are considered as inputdata. Seven constant parameters are calculated as output data of thecalibration. From the transformed raw values a polynomial is calculatedand the coefficient of the polynomial are stored in the integralcircuit. During operation of the sensor, the digital equivalents of thephysical input variables of the pressure and the temperature areconverted into the respective values in bar and ° C. by means of asecond-order polynomial. The parameters calculated during calibrationare used as coefficients in this polynomial.

FIG. 2 illustrates a method according to an embodiment of the presentinvention. Specifically, values of at least one measurand, e.g. apressure or a vibration, at at least two different temperatures arerecorded for determining a sensor characteristic (8). An optimumcharacteristic line, e.g. a parabola in the case of pressure, can bedetermined from the plurality of temperature-depending curves ofpressure raw values, which parabola may be located on average betweenthe curves of a humid state and a dry state, for example. The extent ofthe influence of the further influencing value, humidity for example, ona sensor can be determined from the distance of the pressure raw valuecurve in the dry state to the averaged optimum raw value curve.Determination of the distances between the two curves, e.g. pressurecurves, can be effected at three different temperatures, for example. Inorder to not have to perform this method for every sensor to becalibrated, the extent of the influence of the further influencing valuecan be determined form a sensor characteristic, such as the temperaturesensitivity of the pressure raw values (9).

For this purpose, the secant of a raw value curve of a sensor isdetermined between at least two temperature values. The slope of thissecant indicates the sensitivity of the measuring signal, here forexample the sensitivity of the pressure signal relative to a change intemperature. This concept is referred to as sensitivity.

By means of the significant selection of sensors, a linear relationbetween the sensitivity and the extent of the influence of humidity atthe different temperatures was found. That means that the extent of theinfluence of the humidity can be concluded from the sensitivity that canbe determined from the sensor characteristic in a simple manner. Thisway, a compensation of the humidity influence can be calculateddepending on the sensitivity (11). For example the pressure raw valuesmeasured for calibration of each sensor are corrected in direction ofthe optimum averaged parabola by means of linear transformations whichhave been determined based on the significant selection of sensors (10).

All features indicated in the above description and in the claims can becombined in any selection with the features of the independent claim.The disclosure of the invention is thus not limited to the described orclaimed feature combinations, rather all reasonable feature combinationsdisclosed within the scope of the invention are to be considered asdisclosed.

The invention claimed is:
 1. A method for calibrating at least onestructure borne sound sensor with a membrane and a sensor elementarranged on the membrane having at least one signal-conductingconnection to at least one signal converter, wherein a sensorcharacteristic is recorded by the determination of at least onemeasurand at at least two different temperatures, the extent of theinfluence of a further value influencing the sensor is determined fromthe sensor characteristics via a functional relation, the extent of theinfluence of the further influencing value is considered forcalibration, the influence of the further influencing value iscompensated in the calibration; the extent of the influence of thefurther influencing value corresponds to the intervals of the raw valuesof the measurand recorded by the sensor for corresponding averagedvalues located on a predetermined characteristic curve at at least twotemperature values; and the predetermined characteristic curve is anaveraged characteristic curve of the raw values (1-7) of the measurandat a varying temperature which is determined at a selection ofidentically constructed sensors, wherein the respective characteristiccurves are recorded when wet sensors are drying and the average value ofthe characteristic curves is determined.
 2. The method according toclaim 1, wherein the secant of the raw values of the measurand locatedon a functional graph is calculated between at least two temperaturevalues, the slope of the secant is determined and that the sensitivityof the raw values of the measurand to a temperature change is concludedfrom the slope of the secant.
 3. The method according to claim 1,wherein a linear relation is assumed between the extent of the influenceof the further influencing value and the sensitivity of the raw valuesof the measurand to a temperature change.
 4. The method according toclaim 1, wherein the further value influencing the sensor is humidityacting on the sensor.
 5. The method according to claim 1, wherein acompensation of the influence of the further influencing value iscalculated in the calibration depending on the sensitivity of themeasurand to temperature changes and that the raw values are shifted indirection of the predetermined characteristic curve for compensating theinfluence.
 6. The method according to claim 5, wherein the correction ofthe raw values in direction of the predetermined characteristic curve iseffected by means of at least one linear transformation which indicatesthe distance to the predetermined characteristic curve for at least onesensitivity value at at least one temperature.